There are many research questions about effects of naturally0ccuring behaviors (e.g. dieting) on overweight and obesity for which a randomized controlled trial would be untenable. For these we must rely on observational studies, and causal inference with observational data poses difficult methodological challenges. Many techniques have been proposed for estimating average causal effects, but tools for modeling variation in these effects are scarce. In this R21 project, we will develop a new methodology for causal regression which allows effects at the individual level to covary with characteristics of individuals, contexts and environments. Our formulation is similar to that of the marginal structural model developed by James Robins et al., but we propose a new estimation technique based on imputation rather than weighting. We will obtain estimates and standard errors, diagnostics to help users identify shortcomings in the model, and methods to handle data from surveys with complex design features (strata, clusters, unequal probabilities of selection). Methods will be implemented in a user-friendly software and made available to health-outcome researchers. Secondary analyses will be performed on three epidemiologic datasets to assess the variation in effects dietary restraint, physical activity and other behaviors on body weight and other sequelae in adolescence and young adulthood. PUBLIC HEALTH RELEVANCE: This project will (a) generate new statistical methods and software to help obesity researchers draw robust conclusions about effects of behaviors and treatments that have not been randomized, and (b) apply these methods in secondary analyses of observational data on weight-related behaviors in adolescence and young adulthood. Methods and findings will inform the design and implementation of more effective interventions for treatment and prevention of obesity.